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In order to facilitate deterministic behavior; industrial real-time communication stacks need another design than non-real-time communication stacks typically found in e.g., Internet of Things and Cloud solutions. We propose a flexible stack design that enable code reuse between testbeds and simulators, as well as how stack layers are driven and prioritized. The design can be generalized and used for non-real-time bare-metal solutions like battery powered Internet of Things. Our approach aims at extending the use of simulation during development of industrial systems in order to find logical errors and wrong assumptions earlier in the development. Conducted and evaluated experiments show that the proposed solutions are able to extend the use of simulation during development of real-time communication software. This is achieved by reusing the same code on an industrial testbed and in a discrete event simulator. In addition, the experiments show that the stack design is generalizable and enable reuse with other non-real-time embedded systems. The contribution consists of a set of building blocks for real-time systems that enable control over the system timing when executing on a simulation host while reusing the source code from an industrial testbed. Overall, this will improve the engineering situation, with respect to code reuse, flexibility and debugging. 

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. The potential business values that cloud consumers can achieve depend a lot on the quality of service in the provided cloud services. Therefore, ensuring the quality of service through service-level agreements (SLA) for such cloud-based services is crucial for both the service providers and service consumers. As SLA is critical for cloud deployments and wider adoption of cloud services, the management of SLA in cloud and IoT has thus become an important and essential issue. In this paper we provide an understanding of the current status and maturity level of SLA management in industrial IoT and academic efforts in this field. We also conduct a preliminary survey of current research on SLA management in order to identify open challenges and gaps that need to be addressed in future research directions. In particular, we investigate how to provide useful SLA management support adapted to the maturity level and current industrial practices, and shorten the gap between academia and industry.

Cloud computing and Internet of Things (IoT) are two notable concepts that have evolved significantly over the past few years. In the automation industry, clouds are often used for monitoring vast amounts of data generated on the shop floor. Whereas, IoT is used to simplify the end devices and their connections to the rest of the system. In this paper we investigate the interplay of these two concepts and their use in the control applications in the automation industry. We develop a prototype in the industrial setup to explore the use of IoT devices that communicate with a cloud-based controller. Using the prototype, we perform a number of experiments to investigate the consequences of having a cloud server between the end device and the controller. Within this context we consider arbitrary jitter and delays, i.e., they can be smaller, equal or greater than the sampling periods. Moreover, we apply mitigation mechanisms to deal with the delays and jitter that are caused by the local and wide area networks (LAN and WAN).

The evolution of Cloud Computing motivates teaching this subject to computer science senior students and graduate students so that they can gain broad exposure to the main body of knowledge of Cloud Computing and get prepared for occupations in industry. We started the development of such a course from the end of 2013. To efficiently develop a course that introduces a new technology and has a good balance of a theoretical base and practical experience, we have designed such a course in two steps, by giving a course to the third cycle education and then to the second cycle education after refinement. In this paper, we report on our experiences gained from giving the course to the third cycle education and our reflections from the experience. We also discuss what we can improve on the next course occasion and for the second cycle education on this subject.

Using internet of things (IoT) to connect things, service, and people for intelligent operations has been discussed and deployed in many industry domains such as smart city, smart energy, healthcare, food and water tracking, logistics and retail, and transportation. However, scarce information is available for IoT usage in industrial automation domain for reliable and collaborative automation with respect to e.g., enabling scalable collaboration between heterogeneous devices and systems, offering predictable and fault-tolerant real-time closed-loop control, and inclusion of intelligent service features from edge devices to the cloud. In this paper, we will clarify the specific quality attribute constraints within industrial automation, present specific industrial IoT challenges due to these constraints, and discuss the potentials of utilizing some technical solutions to cope with these challenges.

Cloud Computing is changing the services consumption and delivery platform as well as the way businesses and users interact with IT resources. It represents a major conceptual shift that introduces new elements in programming models and development environments that are not present in traditional technologies. The evolution of Cloud Computing motivates teaching Cloud Computing to computer science senior students and graduate students so that they can gain broad exposure to the main body of knowledge of Cloud Computing and get prepared for occupations in industry. There is thus a strong need for having a Cloud Computing education course that (i) has a broad coverage of different roles interacting with a cloud; and (ii) leverages Cloud Computing concepts, technology and architecture topics at both introductory and advanced level. In this paper, we describe the demand for understanding the impact of Cloud Computing in computer science higher education. We propose education strategies for teaching Cloud Computing, including key knowledge areas for an enduring Cloud Computing course. © 2014 IEEE.

Today’s industrial control systems store large amounts of monitored sensor data in order to optimize industrial processes. In the last decades, architects have designed such systems mainly under the assumption that they operate in closed, plant-side IT infrastructures without horizontal scalability. Cloud technologies could be used in this context to save local IT costs and enable higher scalability, but their maturity for industrial applications with high requirements for responsiveness and robustness is not yet well understood. We propose a conceptual architecture as a basis to designing cloud-native monitoring systems. As a first step we benchmarked three open source timeseries databases (OpenTSDB, KairosDB and Databus) on cloud infrastructures with up to 36 nodes with workloads from realistic industrial applications. We found that at least KairosDB fulfills our initial hypotheses concerning scalability and reliability.

Performing system test for large-scale industrial systems is a challenging activity due to the complexity involved in managing the variety of distributed hardware systems in general, and the hardware-related challenges in test environment in particular. Virtualization technology opens up the possibility to address these challenges, e.g., with respect to cost efficient scalability. In this paper, we identify hardware-related challenges in the test environment for industrial automation systems, identify relevant research studies that address these issues using virtualization technology, and analyze their applicability in the industry domain. In addition, we analyze the impacts of virtualization on essential industrial system requirements with respect to performance, timing, reliability, availability, and safety in the industrial automation domain, and we discuss further limitations in the virtualized test environment.

The technique of virtualization and cloud computing to manage system functionality and resources regardless of their physical locations is changing the way businesses and users interact with IT resources. Although several commercially available virtualization solutions already exist in the market, both for server and embedded real-time based systems, the deployment of virtualization and cloud-based technologies into the industrial automation domain is new. In this paper, we will first present the emerging trends of industrial automation domain and identify the architectural sustainability challenges that follow. Based on these challenges, we will then analyze how virtualization technology can contribute to cope with them, as well as the additional opportunities that it brings to industrial automation domain. The contributions of this paper are (1) to communicate the main trends happening in industrial automation, (2) clarify the architecture sustainability challenges that the automation domain is facing, and (3) identify the potentials of further utilizing virtualization technology in the industry domain.